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Using Stable Diffusion with Python
Using Stable Diffusion with Python

Using Stable Diffusion with Python : Leverage Python to control and automate high-quality AI image generation using Stable Diffusion

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Profile Icon Andrew Zhu (Shudong Zhu)
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$30.99 $44.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (5 Ratings)
Paperback Jun 2024 352 pages 1st Edition
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Arrow left icon
Profile Icon Andrew Zhu (Shudong Zhu)
Arrow right icon
$30.99 $44.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (5 Ratings)
Paperback Jun 2024 352 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$30.99 $44.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$24.99 $35.99
Paperback
$30.99 $44.99
Subscription
Free Trial
Renews at $19.99p/m

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Using Stable Diffusion with Python

Part 1 – A Whirlwind of Stable Diffusion

Welcome to the fascinating world of Stable Diffusion, a rapidly evolving field that has revolutionized the way we approach image generation and manipulation. In the first part of our journey, we’ll embark on a comprehensive exploration of the fundamentals, laying the groundwork for a deep understanding of this powerful technology.

Over the next six chapters, we’ll delve into the core concepts, principles, and applications of Stable Diffusion, providing a solid foundation for further experimentation and innovation. We’ll begin by introducing the basics of Stable Diffusion, followed by a hands-on guide to setting up your environment for success. You’ll then learn how to generate stunning images using Stable Diffusion, before diving deeper into the theoretical underpinnings of diffusion models and the intricacies of how Stable Diffusion works its magic.

By the end of this part, you’ll possess a broad understanding of Stable Diffusion, from its underlying mechanics to practical applications, empowering you to harness its potential and create remarkable visual content. So, let’s dive in and discover the wonders of Stable Diffusion!

This part contains the following chapters:

  • Chapter 1, Introducing Stable Diffusion
  • Chapter 2, Setup Environment for Stable Diffusion
  • Chapter 3, Generate Images Using Stable Diffusion
  • Chapter 4, Understand the Theory behind the Diffusion Models
  • Chapter 5, Understanding How Stable Diffusion Works
  • Chapter 6, Using the Stable Diffusion Model
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Key benefits

  • Master the art of generating stunning AI artwork with the help of expert guidance and ready-to-run Python code
  • Get instant access to emerging extensions and open-source models
  • Leverage the power of community-shared models and LoRA to produce high-quality images that captivate audiences
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Stable Diffusion is a game-changing AI tool that enables you to create stunning images with code. The author, a seasoned Microsoft applied data scientist and contributor to the Hugging Face Diffusers library, leverages his 15+ years of experience to help you master Stable Diffusion by understanding the underlying concepts and techniques. You’ll be introduced to Stable Diffusion, grasp the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. Covering techniques such as face restoration, image upscaling, and image restoration, you’ll focus on unlocking prompt limitations, scheduled prompt parsing, and weighted prompts to create a fully customized and industry-level Stable Diffusion app. This book also looks into real-world applications in medical imaging, remote sensing, and photo enhancement. Finally, you'll gain insights into extracting generation data, ensuring data persistence, and leveraging AI models like BLIP for image description extraction. By the end of this book, you'll be able to use Python to generate and edit images and leverage solutions to build Stable Diffusion apps for your business and users.

Who is this book for?

If you're looking to gain control over AI image generation, particularly through the diffusion model, this book is for you. Moreover, data scientists, ML engineers, researchers, and Python application developers seeking to create AI image generation applications based on the Stable Diffusion framework can benefit from the insights provided in the book.

What you will learn

  • Explore core concepts and applications of Stable Diffusion and set up your environment for success
  • Refine performance, manage VRAM usage, and leverage community-driven resources like LoRAs and textual inversion
  • Harness the power of ControlNet, IP-Adapter, and other methodologies to generate images with unprecedented control and quality
  • Explore developments in Stable Diffusion such as video generation using AnimateDiff
  • Write effective prompts and leverage LLMs to automate the process
  • Discover how to train a Stable Diffusion LoRA from scratch
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Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 03, 2024
Length: 352 pages
Edition : 1st
Language : English
ISBN-13 : 9781835086377
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What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Colour book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
Estimated delivery fee Deliver to South Korea

Standard delivery 10 - 13 business days

$12.95

Premium delivery 5 - 8 business days

$45.95
(Includes tracking information)

Product Details

Publication date : Jun 03, 2024
Length: 352 pages
Edition : 1st
Language : English
ISBN-13 : 9781835086377
Category :
Languages :
Concepts :

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Table of Contents

28 Chapters
Part 1 – A Whirlwind of Stable Diffusion Chevron down icon Chevron up icon
Chapter 1: Introducing Stable Diffusion Chevron down icon Chevron up icon
Chapter 2: Setting Up the Environment for Stable Diffusion Chevron down icon Chevron up icon
Chapter 3: Generating Images Using Stable Diffusion Chevron down icon Chevron up icon
Chapter 4: Understanding the Theory Behind Diffusion Models Chevron down icon Chevron up icon
Chapter 5: Understanding How Stable Diffusion Works Chevron down icon Chevron up icon
Chapter 6: Using Stable Diffusion Models Chevron down icon Chevron up icon
Part 2 – Improving Diffusers with Custom Features Chevron down icon Chevron up icon
Chapter 7: Optimizing Performance and VRAM Usage Chevron down icon Chevron up icon
Chapter 8: Using Community-Shared LoRAs Chevron down icon Chevron up icon
Chapter 9: Using Textual Inversion Chevron down icon Chevron up icon
Chapter 10: Overcoming 77-Token Limitations and Enabling Prompt Weighting Chevron down icon Chevron up icon
Chapter 11: Image Restore and Super-Resolution Chevron down icon Chevron up icon
Chapter 12: Scheduled Prompt Parsing Chevron down icon Chevron up icon
Part 3 – Advanced Topics Chevron down icon Chevron up icon
Chapter 13: Generating Images with ControlNet Chevron down icon Chevron up icon
Chapter 14: Generating Video Using Stable Diffusion Chevron down icon Chevron up icon
Chapter 15: Generating Image Descriptions Using BLIP-2 and LLaVA Chevron down icon Chevron up icon
Chapter 16: Exploring Stable Diffusion XL Chevron down icon Chevron up icon
Chapter 17: Building Optimized Prompts for Stable Diffusion Chevron down icon Chevron up icon
Part 4 – Building Stable Diffusion into an Application Chevron down icon Chevron up icon
Chapter 18: Applications – Object Editing and Style Transferring Chevron down icon Chevron up icon
Chapter 19: Generation Data Persistence Chevron down icon Chevron up icon
Chapter 20: Creating Interactive User Interfaces Chevron down icon Chevron up icon
Chapter 21: Diffusion Model Transfer Learning Chevron down icon Chevron up icon
Chapter 22: Exploring Beyond Stable Diffusion Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(5 Ratings)
5 star 80%
4 star 20%
3 star 0%
2 star 0%
1 star 0%
Harshavardhan K Jul 03, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I recently picked up this book, and it's been a game-changer for me. If you're into AI image generation, this book is the new gold.They walk you through setting up everything you need - CUDA, PyTorch, the works - without making it feel like a slog. And when they get into the nitty-gritty of diffusion models, they somehow make it all click. The authors have this knack for explaining tricky concepts in a way that just makes sense. Whether you're just dipping your toes into this stuff or you've been at it for a while, you'll get something out of this book.All in all, if you're even slightly curious about AI image generation, grab a copy. Trust me, you won't regret it!
Amazon Verified review Amazon
Chenxi L. Jul 05, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
From the outset, the author sets a clear and engaging tone, making complex concepts accessible to a broad audience. The book's structure is well-organized, beginning with an introduction to the fundamentals of stable diffusion processes and their significance in various applications. This foundational knowledge is crucial for readers who may not have a strong background in statistics or stochastic processes.One of the book's standout features is its practical approach. The author does an excellent job of not just explaining theoretical concepts but also demonstrating how to implement them using Python. The step-by-step examples and code snippets are particularly useful, providing readers with hands-on experience and making it easy to follow along. The inclusion of real-world applications further enhances the learning experience, showcasing how these techniques can resolve practical issues and save valuable time.
Amazon Verified review Amazon
Petar Dimov Sep 13, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
💡 My exploration through Shudong Zhu’s comprehensive guide has been both informative and inspiring.✍ 𝗧𝗵𝗲 𝗯𝗼𝗼𝗸 𝗯𝗲𝗴𝗶𝗻𝘀 𝗯𝘆 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗼𝗳 𝗦𝘁𝗮𝗯𝗹𝗲 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻, providing a solid foundation on its evolution, setup, and practical image generation techniques. The initial sections cover everything from the theory behind diffusion models to hands-on image creation.👨‍💻 𝗣𝗮𝗿𝘁 𝟮 𝗱𝗶𝘃𝗲𝘀 𝗶𝗻𝘁𝗼 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗮𝗻𝗱 𝗰𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗹𝗲 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻. It offers strategies to enhance performance, leverage community-driven resources like LoRAs, and tackle advanced features such as image restoration and prompt management.⚖️ 𝗧𝗵𝗲 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗣𝗮𝗿𝘁 𝟯 𝗽𝘂𝘀𝗵 𝘁𝗵𝗲 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀 𝗼𝗳 𝘄𝗵𝗮𝘁 𝗦𝘁𝗮𝗯𝗹𝗲 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝗰𝗮𝗻 𝗱𝗼. It covers the integration of ControlNet, video generation, image description with BLIP-2 and LLaVA, and the new capabilities of Stable Diffusion XL. The section also addresses prompt optimization techniques for improved results.🛠️ 𝗜𝗻 𝘁𝗵𝗲 𝗳𝗶𝗻𝗮𝗹 𝘀𝗲𝗰𝘁𝗶𝗼𝗻, 𝗣𝗮𝗿𝘁 𝟰, 𝘁𝗵𝗲 𝗯𝗼𝗼𝗸 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻𝘀 𝗶𝗻𝘁𝗼 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. It discusses object editing, style transferring, data persistence, and building interactive UIs with Gradio, concluding with insights on diffusion model transfer learning and the broader implications of AI advancements.✨ Overall, "𝗨𝘀𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗹𝗲 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻" is an essential resource for anyone interested in mastering Stable Diffusion. It combines foundational theory with practical applications, making it a valuable guide for both newcomers and experienced practitioners in the field of AI image generation.
Amazon Verified review Amazon
R V Jun 06, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you ever tried to write your own application for Stable Diffusion, you'd notice that the prompt is limited to only 77 tokens, and you will receive no warning. You need to chunk your big prompt and use a package like Compel to make it work. I learned it the hard way, the book would have told me immediately. Adding LoRas and LyCos are not as easy as doing so in A1111 or ComfyUI. I only hoped I had this book way before it was launched. The author is one of the developers of the Diffusers library and he knows what he is doing. He is also a talented writer. It's not a beginner's book and you are buying it because you are hard core programmer. I bought this book twice in the pre-order by mistake and received a copy as present from a great friend.
Amazon Verified review Amazon
Steven Fernandes Aug 06, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book outlines strategies to refine performance, manage VRAM effectively, and utilize community innovations like LoRAs and textual inversion. It introduces powerful tools like ControlNet and IP-Adapter, enhancing control and quality in image generation. The exploration extends to video creation with AnimateDiff and the art of crafting effective prompts, including the use of LLMs for automation. Additionally, it offers a detailed walkthrough on training a Stable Diffusion LoRA from the ground up, making it an invaluable resource for both novice and advanced users in the field of AI-driven art.
Amazon Verified review Amazon